That's another story, but I will say, that our home held up great through it all, and we learned the true meaning of Mobile Home. When we purchased our home inthe exterior looked pretty rough, but we could see past it and knew with a few improvements, it could look almost as good as new. This is how our house looked on the day we purchased it.
That could be a problem. Instead, it relied entirely on an algorithm that had taught itself to drive by watching a human do it. Getting a car to drive this way was an impressive feat. But what if one day it did something unexpected—crashed into a tree, or sat at a green light?
As things stand now, it might be difficult to find out why. The system is so complicated that even the engineers who designed it may struggle to isolate the reason for any single action. The mysterious mind of this vehicle points to a looming issue with artificial intelligence.
There is now hope that the same techniques will be able to diagnose deadly diseases, make million-dollar trading decisions, and do countless other things to transform whole industries. If you could get access to these mathematical models, it would be possible to understand their reasoning.
But banks, the military, employers, and others are now turning their attention to more complex machine-learning approaches that could make automated decision-making altogether inscrutable.
Deep learning, the most common of these approaches, represents a fundamentally different way to program computers. Starting in the summer ofthe European Union may require that companies be able to give users an explanation for decisions that automated systems reach.
This might be impossible, even for systems that seem relatively simple on the surface, such as the apps and websites that use deep learning to serve ads or recommend songs. The computers that run those services have programmed themselves, and they have done it in ways we cannot understand.
Even the engineers who build these apps cannot fully explain their behavior. This raises mind-boggling questions.
As the technology advances, we might soon cross some threshold beyond which using AI requires a leap of faith. Will that also be possible with machines that think and make decisions differently from the way a human would?
How well can we expect to communicate—and get along with—intelligent machines that could be unpredictable and inscrutable? These questions took me on a journey to the bleeding edge of research on AI algorithms, from Google to Apple and many places in between, including a meeting with one of the great philosophers of our time.
The artist Adam Ferriss created this image, and the one below, using Google Deep Dream, a program that adjusts an image to stimulate the pattern recognition capabilities of a deep neural network. The pictures were produced using a mid-level layer of the neural network. This data set features hundreds of variables on patients, drawn from their test results, doctor visits, and so on.
The resulting program, which the researchers named Deep Patient, was trained using data from aboutindividuals, and when tested on new records, it proved incredibly good at predicting disease. Without any expert instruction, Deep Patient had discovered patterns hidden in the hospital data that seemed to indicate when people were on the way to a wide range of ailments, including cancer of the liver.
It appears to anticipate the onset of psychiatric disorders like schizophrenia surprisingly well. But since schizophrenia is notoriously difficult for physicians to predict, Dudley wondered how this was possible.
The new tool offers no clue as to how it does this.
If something like Deep Patient is actually going to help doctors, it will ideally give them the rationale for its prediction, to reassure them that it is accurate and to justify, say, a change in the drugs someone is being prescribed.
From the outset, there were two schools of thought regarding how understandable, or explainable, AI ought to be. Many thought it made the most sense to build machines that reasoned according to rules and logic, making their inner workings transparent to anyone who cared to examine some code.
Others felt that intelligence would more easily emerge if machines took inspiration from biology, and learned by observing and experiencing.
This meant turning computer programming on its head. Instead of a programmer writing the commands to solve a problem, the program generates its own algorithm based on example data and a desired output.
Then the computerization of many industries and the emergence of large data sets renewed interest. That inspired the development of more powerful machine-learning techniques, especially new versions of one known as the artificial neural network.
By the s, neural networks could automatically digitize handwritten characters.Jun 15, · Mix - MY HEART IS SO FULL OF YOU: Liz Callaway YouTube OBSESSED!: Dissed by Meg Ryan, Liz Callaway Talks to Her Cat and Becomes a Better Singer - Duration: Jan 10, · Hello there!
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Performing arts are not always a priority in schools. In fact, this is true for children in elementary school all the way through high school.